The optimization of condition-based maintenance (CBM) poses challenges due to the rapid advancement of monitoring technologies. Traditional CBM research has mainly relied on theory-driven approaches, which lead to the...The optimization of condition-based maintenance (CBM) poses challenges due to the rapid advancement of monitoring technologies. Traditional CBM research has mainly relied on theory-driven approaches, which lead to the development of several effective maintenance models characterized by their wide applicability and attractiveness. However, when the system reliability model becomes complex, such methods may run into intractable cost models. The Markov decision process (MDP), a classic framework for sequential decision making, has drawn increasing attention for optimization of CBM optimization due to its appealing tractability and pragmatic applicability across different problems. This paper presents a review of research that optimizes CBM policies using MDP, with a focus on mathematical modeling and optimization methods to enable action. We have organized the review around several key components that are subject to similar mathematical modeling constraints, including system complexity, the availability of system conditions, and diverse criteria of decision-makers. An increase in interest has led to the optimization of CBM for systems possessing increasing numbers of components and sensors. Then, the review focuses on joint optimization problems with CBM. Finally, as an important extension to traditional MDPs, reinforcement learning (RL) based methods are also reviewed as ways to optimize CBM policies. This paper provides significant background research for researchers and practitioners working in reliability and maintenance management, and gives discussions on possible future research directions.展开更多
Climate change has intensified extreme rainfall events,challenging progress toward SDG 11’s urban resilience targets.Current assessment methods often neglect dynamic recovery processes and regional precipitation disp...Climate change has intensified extreme rainfall events,challenging progress toward SDG 11’s urban resilience targets.Current assessment methods often neglect dynamic recovery processes and regional precipitation disparities.We propose a three-phase framework combining interpretable machine learning(ML)and factorial experiments,using the Prep_shock index that integrates standardized rainfall intensity,capital exposure,and historical probability,to evaluate the dynamic resilience of 220+Chinese cities from 2019 to 2022.Key findings reveal that:(1)The Prep_shock index effectively eliminates north-south precipitation biases,identifying Shandong coastal cities and Yangtze River Delta city clusters(36.2%)as high-resilience areas,in contrast to Henan Province.COVID-19 exacerbated systemic risks in megacities,undermining their capital protection capacities.(2)Spatial diagnostics classify 75.6%of the cities into QuadrantⅢ(the balanced resilience category),with recovery times decreasing from the west to the east.Super-large cities like Zhengzhou(2021)exhibited critical recovery deficiencies(QuadrantⅣ).(3)Interpretable ML models(XGBoost/EBM)identify redundancy as the dominant resilience driver—robustness governs baseline resilience,while recovery relies on emergency support(for example,hospital beds density and fiscal inputs)and redundant infrastructure(for example,road network density).(4)Factorial experiments reveal optimization trade-offs:simultaneous enhancement of rapidity and redundancy diminishes their individual benefits,necessitating context-specific prioritization.The study advances dynamic resilience assessment methods and proposes quadrant-specific strategies for tailored urban adaptation.展开更多
Defining and measuring resilience using a unified framework has been a topic of intense research.This article presents a perspective on how resilience could be quantitatively assessed through a set of indices.It start...Defining and measuring resilience using a unified framework has been a topic of intense research.This article presents a perspective on how resilience could be quantitatively assessed through a set of indices.It starts with a brief explanation of resilience in the context of supply chain and a quick summary of existing quantitative measures of resilience.It then discusses how resilience could be quantified in a constructive manner so that the resulting metrics are representative of the performance throughout the system's life cycle.In particular,it is proposed that resilience should be evaluated according to different time periods,i.e.before,during and after a disruption has occurred.Four dimensions of resilience,namely reliability,robustness,recovery and reconfigurability,can then be used to make up a set of indices for resilience.For numerical illustration,these indices are computed based on recovery data arising from Hurricane Sandy in October 2012.Finally,it is postulated that resilience will be the performance metric that complements productivity and sustainability as the third pillar for measuring success of organizations,and in turn,that of sovereign countries in their quests for developing smart cities.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.72401253,72371182,72002149,and 72271154)and the National Social Science Fund of China(23CGL018)+1 种基金the State Key Laboratory of Biobased Transportation Fuel Technology,China(Grant No.512302-X02301)a start-up grant from the ZJU-UIUC Institute at Zhejiang University(Grant No.130200-171207711).
文摘The optimization of condition-based maintenance (CBM) poses challenges due to the rapid advancement of monitoring technologies. Traditional CBM research has mainly relied on theory-driven approaches, which lead to the development of several effective maintenance models characterized by their wide applicability and attractiveness. However, when the system reliability model becomes complex, such methods may run into intractable cost models. The Markov decision process (MDP), a classic framework for sequential decision making, has drawn increasing attention for optimization of CBM optimization due to its appealing tractability and pragmatic applicability across different problems. This paper presents a review of research that optimizes CBM policies using MDP, with a focus on mathematical modeling and optimization methods to enable action. We have organized the review around several key components that are subject to similar mathematical modeling constraints, including system complexity, the availability of system conditions, and diverse criteria of decision-makers. An increase in interest has led to the optimization of CBM for systems possessing increasing numbers of components and sensors. Then, the review focuses on joint optimization problems with CBM. Finally, as an important extension to traditional MDPs, reinforcement learning (RL) based methods are also reviewed as ways to optimize CBM policies. This paper provides significant background research for researchers and practitioners working in reliability and maintenance management, and gives discussions on possible future research directions.
基金supported by the National Key R&D Program of China(Grant No.2024YFC3014100)the National Natural Science Foundation of China(Grant No.52394232)+1 种基金the General Program of National Natural Science Foundation of China(Grant No.42377467)the Fundamental Research Funds for the Central Universities(Grant No.2243300007).
文摘Climate change has intensified extreme rainfall events,challenging progress toward SDG 11’s urban resilience targets.Current assessment methods often neglect dynamic recovery processes and regional precipitation disparities.We propose a three-phase framework combining interpretable machine learning(ML)and factorial experiments,using the Prep_shock index that integrates standardized rainfall intensity,capital exposure,and historical probability,to evaluate the dynamic resilience of 220+Chinese cities from 2019 to 2022.Key findings reveal that:(1)The Prep_shock index effectively eliminates north-south precipitation biases,identifying Shandong coastal cities and Yangtze River Delta city clusters(36.2%)as high-resilience areas,in contrast to Henan Province.COVID-19 exacerbated systemic risks in megacities,undermining their capital protection capacities.(2)Spatial diagnostics classify 75.6%of the cities into QuadrantⅢ(the balanced resilience category),with recovery times decreasing from the west to the east.Super-large cities like Zhengzhou(2021)exhibited critical recovery deficiencies(QuadrantⅣ).(3)Interpretable ML models(XGBoost/EBM)identify redundancy as the dominant resilience driver—robustness governs baseline resilience,while recovery relies on emergency support(for example,hospital beds density and fiscal inputs)and redundant infrastructure(for example,road network density).(4)Factorial experiments reveal optimization trade-offs:simultaneous enhancement of rapidity and redundancy diminishes their individual benefits,necessitating context-specific prioritization.The study advances dynamic resilience assessment methods and proposes quadrant-specific strategies for tailored urban adaptation.
基金This work is supported by the National Research Foundation,Prime Minister's Office,Singapore under its Campus for Research Excellence and Technological Enterprise(CREATE)program on Future Resilient Systems phase 2(FRS2).
文摘Defining and measuring resilience using a unified framework has been a topic of intense research.This article presents a perspective on how resilience could be quantitatively assessed through a set of indices.It starts with a brief explanation of resilience in the context of supply chain and a quick summary of existing quantitative measures of resilience.It then discusses how resilience could be quantified in a constructive manner so that the resulting metrics are representative of the performance throughout the system's life cycle.In particular,it is proposed that resilience should be evaluated according to different time periods,i.e.before,during and after a disruption has occurred.Four dimensions of resilience,namely reliability,robustness,recovery and reconfigurability,can then be used to make up a set of indices for resilience.For numerical illustration,these indices are computed based on recovery data arising from Hurricane Sandy in October 2012.Finally,it is postulated that resilience will be the performance metric that complements productivity and sustainability as the third pillar for measuring success of organizations,and in turn,that of sovereign countries in their quests for developing smart cities.
